import numpy as np

# V5_5_0.01_Sep30_17-02-44_wz209
# V5___K-fold:5___lr:0.01___save:../101
# 6个损失函数,3种损失,数据增强的方式为左右\上下翻转
a1d = [0.8998260345976163, 0.8349603012716824, 0.9623702060875984, 22]
a1j = [0.8998260345976163, 0.8349603012716824, 0.9623702060875984, 22]
a1a = [0.838117196318678, 0.7518218346523395, 0.9448366202824257, 0]
#
#
# V5_4_0.01_Sep30_17-03-53_wz209
# V5___K-fold:4___lr:0.01___save:../102
# 6个损失函数,3种损失,数据增强的方式为左右\上下翻转
a2d = [0.8979555345160676, 0.8302098389227579, 0.9612832777434007, 41]
a2j = [0.8979555345160676, 0.8302098389227579, 0.9612832777434007, 41]
a2a = [0.8617345106630702, 0.7810345098608181, 0.945967275595378, 0]
#
#
# V5_3_0.01_Sep30_17-04-12_wz209
# V5___K-fold:3___lr:0.01___save:../103
# 6个损失函数,3种损失,数据增强的方式为左右\上下翻转
a3d = [0.9038259315382098, 0.8369998323134623, 0.9621496182763851, 41]
a3j = [0.9038259315382098, 0.8369998323134623, 0.9621496182763851, 41]
a3a = [0.8607906641395713, 0.777678385924336, 0.9426640858725759, 0]
#
#
#
# V5_2_0.01_Sep30_23-09-37_wz209
# V5___K-fold:2___lr:0.01___save:../104
# 6个损失函数,3种损失,数据增强的方式为左右\上下翻转
a4d = [0.8979469597543, 0.8311188991155603, 0.9653336189225679, 72]
a4j = [0.8977481226772919, 0.8321796226658296, 0.9649176625693362, 35]
a4a = [0.8971826256307899, 0.8302020123450028, 0.9653894208138906, 73]
#
#
#
# V5_1_0.01_Sep30_23-10-30_wz209
# V5___K-fold:1___lr:0.01___save:../105
# 6个损失函数,3种损失,数据增强的方式为左右\上下翻转
a5d = [0.9018386155525383, 0.8351295696889371, 0.9601672105165839, 39]
a5j = [0.9018386155525383, 0.8351295696889371, 0.9601672105165839, 39]
a5a = [0.9018386155525383, 0.8351295696889371, 0.9601672105165839, 39]

average1 = (np.array(a1d)+np.array(a2d)+np.array(a3d)+np.array(a4d)+np.array(a5d))/5*100
print(average1[1],"  ",average1[0],"  ",average1[2])


# V0_3_0.01_Oct07_18-59-19_wz209
# V0___K-fold:3___lr:0.01___save:../203
# 最原始的CPFnet: 一种数据增强+0.01的lr+ploy策略
b3d= [0.9006072696774025, 0.8303462135437256, 0.9626457788232546, 62]
b3j= [0.9006072696774025, 0.8303462135437256, 0.9626457788232546, 62]
b3a= [0.9006072696774025, 0.8303462135437256, 0.9626457788232546, 62]
#
#
#
# V0_4_0.01_Oct07_18-59-44_wz209
# V0___K-fold:4___lr:0.01___save:../202
# 最原始的CPFnet: 一种数据增强+0.01的lr+ploy策略
b4d= [0.89506074446664, 0.8260325031558204, 0.9626403985565998, 59]
b4j= [0.89506074446664, 0.8260325031558204, 0.9626403985565998, 59]
b4a= [0.89506074446664, 0.8260325031558204, 0.9626403985565998, 59]
#
#
#
# V0_5_0.01_Oct07_19-00-00_wz209
# V0___K-fold:5___lr:0.01___save:../201
# 最原始的CPFnet: 一种数据增强+0.01的lr+ploy策略
b5d= [0.8941567831726434, 0.8258465788158849, 0.963016053480811, 95]
b5j= [0.8941567831726434, 0.8258465788158849, 0.963016053480811, 95]
b5a= [0.8938428477606847, 0.8250794877090778, 0.9630732002969046, 79]
#
#
#
# V0_2_0.01_Oct08_00-20-52_wz209
# V0___K-fold:2___lr:0.01___save:../204
# 最原始的CPFnet: 一种数据增强+0.01的lr+ploy策略
b2d = [0.8877240772236917, 0.8175437364679434, 0.9626466608341824, 71]
b2j = [0.8877240772236917, 0.8175437364679434, 0.9626466608341824, 71]
b2a = [0.8862124800909095, 0.8164509417685437, 0.9629838830119857, 53]
#
#
#
# V0_1_0.01_Oct08_00-21-30_wz209
# V0___K-fold:1___lr:0.01___save:../205
# 最原始的CPFnet: 一种数据增强+0.01的lr+ploy策略
b1d= [0.8987443043537119, 0.8288796711197447, 0.9595647970533598, 52]
b1j= [0.8987443043537119, 0.8288796711197447, 0.9595647970533598, 52]
b1a= [0.8958274078176496, 0.8255812652934627, 0.9600178272658939, 63]

average1 = (np.array(b1d)+np.array(b2d)+np.array(b3d)+np.array(b4d)+np.array(b5d))/5*100
print(average1[1], "  ", average1[0], "  ", average1[2])